A neural network approach to predict early neonatal sepsis
| Autor: | Jaime Lorduy Gómez, Edward Rolando Núñez-Valdez, Vicente García-Díaz, Fernando López-Martínez |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
General Computer Science Artificial neural network Neonatal sepsis business.industry Area under the curve 020206 networking & telecommunications 02 engineering and technology medicine.disease Confidence interval Control and Systems Engineering Intensive care Emergency medicine 0202 electrical engineering electronic engineering information engineering Medicine 020201 artificial intelligence & image processing Electrical and Electronic Engineering business Neural network classification Test sample |
| Zdroj: | Computers & Electrical Engineering. 76:379-388 |
| ISSN: | 0045-7906 |
| DOI: | 10.1016/j.compeleceng.2019.04.015 |
| Popis: | The purpose of this study is to develop a non-invasive neural network classification model for early neonatal sepsis detection. Early neonatal sepsis is a public health issue and one of the leading causes of complications and deaths in neonatal intensive care units. The data used in this study is from Crecer’s Hospital center in Cartagena-Colombia. An imbalanced dataset of 555 neonates with (66%) of negative cases and (34%) of positive cases was used for this study. The study results show a sensitivity of 80.32%, a specificity of 90.4%, precision on the positive predicted value of 83.1% in the test sample and a calculated area under the curve of 92.5% (95% Confidence Interval [91.4-93.06]). This neural network model can be used as a smart system’s inference engine to support the detection of neonatal sepsis in neonatal intensive care units. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect